There is a technique which identifies the pattern of an input signal by hierarchically extracting features. This method extracts a high-order feature by using features which form the feature to be extracted and have orders lower than that of the feature to be extracted. Accordingly, the method has the characteristic that it can perform robust identification for the variance of an identification pattern. However, to increase the robustness against the variance of a pattern, it is necessary to increase the number of types of features to be extracted, and this increases the processing cost. If the number of types of features to be extracted is not increased, the possibility of identification errors increases.
To solve the above problems, the following pattern recognition method is proposed. First, feature vectors of patterns of individual classes are arranged in descending order of vector component dispersion to form dictionary patterns, and feature vectors are generated from an input pattern. Then, matching with dictionary patterns of high orders up to the Nth-order is performed. On the basis of the matching result, matching with lower orders is performed. In this manner, the processing cost can be reduced.
The following pattern recognition dictionary formation apparatus and pattern recognition apparatus are also proposed. First, feature vectors are extracted from an input pattern, and classified into clusters in accordance with the degree of matching with the standard vector of each cluster. Category classification is then performed in accordance with the degree of matching between category standard vectors in the classified clusters of the input pattern and the feature vectors. Consequently, the cost of the matching process can be reduced.